This is based on an article of ” Efficient Representation Scheme For Scheme for multidimensional Array Operations” by Chun-Yan lin, Jen-Shiuh Liu and Yeh-ching Chung Member of IEEE published in march 2002.

in there paper they propose a new scheme called **e**xtended **K**arnaugh **m**ap **r**epresentation (EKMR).

there approach can shortly described as they take** t**raditional **m**atrix **r**epresentation on short form TMR, they convert in a 2d array by a very suitable algorithom.

And from these EKMR they generate three different array which consists all the information of TMR taking most common data as a variant so takes less memory.

First take our TMR as a four dimensional array . we need to convert in a two dimentional EKMR, and we want to take ” 0″ as most common data.

so the cpp coding goes as like

//S,P,R,Q are the lentgh of each dimention of TMR #define S 6 #define R 6 #define P 6 #define Q 6//j //declaration of TMR and 2d EKMR long TMR[S][R][P][Q]; long EKMR[S*P][R*Q]; using std::cout; using std::cin; using std::endl; //creation of 4d matrix TKMR void construct() { int l,k,i,j,c=0; for(l=0;l<S;l++)//first dimension array loop { for(k=0;k<R;k++)//second dimension array loop { for(i=0;i<P;i++) //third dimension array loop { for(j=0;j<Q;j++) //4th dimension array loop { c= rand(); //taking a random value for data c=c%10; if(c<=5) // make atleast 50% data is 0 for testing compression {c=0;} TMR[l][k][i][j]=c;//take the value in the array } } } }

now making of TMR to EKMR. or u can say that conversion of a 4D array in 2D array

int l,k,i,j,c=0; for(l=0;l<S;l++) { for(k=0;k<R;k++) { for(i=0;i<P;i++) { for(j=0;j<Q;j++) { TMR[l][k][i][j]= EKMR[i * S + l][j * R + k] ; } } }

now we know “0” is most common . So if we take 3 array and first array consists of all non zero value of EKMR and 2nd array consist of column position of these value and 3rd array consists number of the nonzero value in each row plus already counted row. So the code for this is

long *FW,*FW1,*FW2; // declaration of 3 1D array

int x=0; int y=0; int count=0; int count1=1; int count2=0; int z=1; FW2[0]=0;//assign 3rd arrays first value 0 //one dimensional array created for(int i=0;i<(S*P);i++) { for(int j=0;j<(R*Q);j++) { if(EKMR[i][j]!=0) { FW[x]=EKMR[i][j]; //ekmr value here FW1[y]=j; //valuer position in column x++; // increment of first array y++;//increment of 2nd array } } if(z==1) { FW2[z]=count1-1; } else { FW2[z]=count1+FW2[z-1];//enter total non zero number till } count1=0; z++; //increment of 3rd array }

Now calculate the ratio data compression rate

float ratio= (S*P)*(R*Q); int r=x+y+z; ratio=r/ratio; cout<<"ratio is "<

the rare of data compressed proportionally depend on percentage of common data or the length of dimension.

this technique can be very efficient for image compressing .

so view this table for comparisoncode courtesy to Ashis chakrabatry